This project is designed to detect deepfake voices using machine learning techniques. Deepfake voices are synthetic audio generated by AI, which can be used for fraudulent and malicious purposes. Detecting such voices is crucial for maintaining trust and security in various applications.
In this repository, we provide a Google Colab notebook that guides you through the process of training and deploying a deepfake voice detection model using Python and popular machine learning libraries. You can use this project to detect suspicious voices in audio files or streams.
Before you begin, ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Google Colab account (for cloud GPU resources)
- pycharm
- Clone this repository to your local machine or open it directly in Google Colab.
- Open the Jupyter notebook named
Deepfake_Voice_Detection.ipynb. - Follow the instructions in the notebook to set up your environment and run the code.
To train a deepfake voice detection model, you will need an appropriate dataset. You can use publicly available deepfake voice datasets or create your own. Ensure the dataset is organized and labeled correctly for training.
- Use the provided notebook to preprocess your dataset and train a deepfake voice detection model.
- Experiment with different machine learning algorithms and hyperparameters to optimize your model's performance.
- Once your model is trained, use it for inference by following the notebook's instructions.
- You can perform deepfake voice detection on audio files or streams....
- Evaluate your model's performance using metrics such as accuracy, precision, recall, and F1-score.
- Fine-tune your model based on the evaluation results to improve its accuracy.
You can deploy your deepfake voice detection model for real-time or batch processing applications. This can be done using cloud services, edge devices, or server applications.
Clone the repo then go to the respective directory and type python app.py in the terminal you will get the local host link.